/*
 * Encog(tm) Examples v2.4
 * http://www.heatonresearch.com/encog/
 * http://code.google.com/p/encog-java/
 * 
 * Copyright 2008-2010 by Heaton Research Inc.
 * 
 * Released under the LGPL.
 *
 * This is free software; you can redistribute it and/or modify it
 * under the terms of the GNU Lesser General Public License as
 * published by the Free Software Foundation; either version 2.1 of
 * the License, or (at your option) any later version.
 *
 * This software is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
 * Lesser General Public License for more details.
 *
 * You should have received a copy of the GNU Lesser General Public
 * License along with this software; if not, write to the Free
 * Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA
 * 02110-1301 USA, or see the FSF site: http://www.fsf.org.
 * 
 * Encog and Heaton Research are Trademarks of Heaton Research, Inc.
 * For information on Heaton Research trademarks, visit:
 * 
 * http://www.heatonresearch.com/copyright.html
 */

package crawler.web.engine.neural_network.classifier.feedforward;

import org.encog.neural.data.buffer.BufferedNeuralDataSet;
import org.encog.neural.networks.BasicNetwork;
import org.encog.normalize.DataNormalization;
import org.encog.persist.EncogPersistedCollection;
import org.encog.util.simple.EncogUtility;

public class TrainNetwork {

	public void train() {
		System.out.println("Converting training file to binary");
		//load forest.eg file
		EncogPersistedCollection encog = new EncogPersistedCollection(Constant.TRAINED_NETWORK_FILE);
		//get forest-norm from forest.eg
		DataNormalization norm = (DataNormalization) encog.find(Constant.NORMALIZATION_NAME);
		System.out.println("输入数: " + norm.getNetworkInputLayerSize());
		System.out.println("输出数: " + norm.getNetworkOutputLayerSize());

		//convert normalized.scv file to a binary file, normalized.bin
		//because csv file is ascii that will be converted at every training iteration   
		EncogUtility.convertCSV2Binary(Constant.NORMALIZED_FILE,//
				Constant.BINARY_FILE, norm.getNetworkInputLayerSize(), norm.getNetworkOutputLayerSize(), false);

		// create training set from normalized.bin
		BufferedNeuralDataSet trainingSet = new BufferedNeuralDataSet(Constant.BINARY_FILE);

		//find forest-network from forest.eg
		BasicNetwork network = (BasicNetwork) encog.find(Constant.TRAINED_NETWORK_NAME);
		//create neural network
		if (network == null)
			network = EncogUtility.simpleFeedForward(//
			norm.getNetworkInputLayerSize(),//         input The number of input neurons.
					Constant.HIDDEN_COUNT,//           hidden1 The number of hidden layer 1 neurons. (100)
					0,//                               hidden2 The number of hidden layer 2 neurons.
					norm.getNetworkOutputLayerSize(),//output The number of output neurons.
					false); //tanh True to use hyperbolic tangent activation function, false to use the sigmoid activation function.

		//training neural network
		//parameters:
		//1)network The network to train.
		//2)trainingSet The training set.
		//3)minutes The number of minutes to train for.
		EncogUtility.trainConsole(network, trainingSet, Constant.TRAINING_MINUTES);

		System.out.println("Training complete, saving network...");
		// add forest-network to forest.eg
		// this network is trained and it's weight matrixes are evaluated
		// save network to forest.eg file
		encog.add(Constant.TRAINED_NETWORK_NAME, network);
	}

}
